During the last decade, the use and development of inertial measurement units (IMUs) has consistently increased in the framework of position estimation, motion capture, human-computer interaction, gaming, wearable sports technology, and medical applications, to name a few. In most cases, the IMU sensors are worn by the subject(s) and can constitute a fully portable system that may potentially integrate into a garment. A single IMU typically combines measurements that are synchronously obtained from gyroscopes, magnetometers, and accelerometers. State-of-the-art IMU sensors are thus able to provide time-resolved data on the three-dimensional (3D) acceleration and orientation corresponding to their own placement. The orientation information obtained from one given IMU at one given time point can be decomposed into a tangential surface vector and a relative rotation angle around this vector, i.e., a quaternion. Even though optical means such as multiple cameras and range cameras are now employed to capture anatomical or other 3D information, IMU sensors are advantageously wearable, small, lightweight, and immune to occlusion problems. IMU sensors thus allow for free user movements outside laboratory or similarly controlled environments. Future technological developments even go towards sensors that are integrated into cloth, making them barely noticeable for their users. This makes the use of IMUs potentially suitable to the assessment of daily activities.
Explicit time-stable positional data can hardly be obtained from IMU sensors alone. Indeed, since positions are obtained by double-integrating accelerations, the corresponding estimation error, for example, originating from electronic noise and sensor limitations, is cumulative. In this context, inertial sensors are thus considered in conjunction with a complementary modality that is not subject to similar drift phenomena. In the context of human-activity assessment, for instance, it has been proposed to complement an initial set of IMU sensors with additional ultrasound sensors to reduce the drift that is commonly observed in purely inertial systems. IMU data has also been combined with monocular-depth-camera measurements to reconstruct full-body poses.
Unfortunately, the combination of IMU sensors with other modalities for joint data acquisition tends to make the overall approach cumbersome and impractical. In particular, such hybrid systems are typically not portable, and thus have limited potential impact in terms of applications although they are more accurate than the sole use of IMU sensors. One solution to this dilemma is to restrict the auxiliary acquisition device to an initial acquisition phase. In that case, instead of aiming at directly inferring and exploiting explicit positional data, the goal is to first learn some underlying (less trivial) relationship between the orientation data obtained from the IMUs and the features of interest to be estimated, using the data initially obtained from the auxiliary acquisition device as reference. This approach has been followed for the estimation of features such as global posture, gesture, and position, as well as for activity recognition, based on skeleton models.
The auxiliary modalities that are used mainly consist in 3D-optical-tracking systems, for example, based on range cameras, that provide explicit “ground-truth-type” measurements of the quantities of interest. During training, both inertial and optical data are to be acquired simultaneously. These data are then analyzed to infer underlying relationships between the inertial-sensor readings and the features extracted as ground truth from the optical measurements. Under suitable conditions, this allows to set the optical system aside in subsequent use, thus benefiting from all aforementioned benefits of IMU-based systems. An advantage of learning-based approaches is the robustness to sensor placement.
A clinically relevant application of wearable-system technology is the measurement and analysis of anatomical surfaces, in particular in the field of orthopedics and in the context of pathologies such as back-surface pain or scoliosis. Now, while the aforementioned works to consider the estimation of body features based on skeleton models, they do not deal with the reconstruction of anatomical surfaces per se. Therefore, there is a need in the industry to address one or more of the abovementioned shortcomings.